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Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry cover...

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Autores principales: Abujaber, Ahmad A., Albalkhi, Ibrahem, Imam, Yahia, Nashwan, Abdulqadir J., Yaseen, Said, Akhtar, Naveed, Alkhawaldeh, Ibraheem M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672468/
https://www.ncbi.nlm.nih.gov/pubmed/38003870
http://dx.doi.org/10.3390/jpm13111555
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author Abujaber, Ahmad A.
Albalkhi, Ibrahem
Imam, Yahia
Nashwan, Abdulqadir J.
Yaseen, Said
Akhtar, Naveed
Alkhawaldeh, Ibraheem M.
author_facet Abujaber, Ahmad A.
Albalkhi, Ibrahem
Imam, Yahia
Nashwan, Abdulqadir J.
Yaseen, Said
Akhtar, Naveed
Alkhawaldeh, Ibraheem M.
author_sort Abujaber, Ahmad A.
collection PubMed
description (1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.
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spelling pubmed-106724682023-10-30 Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning Abujaber, Ahmad A. Albalkhi, Ibrahem Imam, Yahia Nashwan, Abdulqadir J. Yaseen, Said Akhtar, Naveed Alkhawaldeh, Ibraheem M. J Pers Med Article (1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors. MDPI 2023-10-30 /pmc/articles/PMC10672468/ /pubmed/38003870 http://dx.doi.org/10.3390/jpm13111555 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abujaber, Ahmad A.
Albalkhi, Ibrahem
Imam, Yahia
Nashwan, Abdulqadir J.
Yaseen, Said
Akhtar, Naveed
Alkhawaldeh, Ibraheem M.
Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title_full Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title_fullStr Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title_full_unstemmed Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title_short Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning
title_sort predicting 90-day prognosis in ischemic stroke patients post thrombolysis using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672468/
https://www.ncbi.nlm.nih.gov/pubmed/38003870
http://dx.doi.org/10.3390/jpm13111555
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